{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:FPAGRIXU6LFYJD4CJIRQXL7HH7","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e7dc15b740888359f3346294c2b9f790c6c2e2c3e6cc8f450bb3ae7b3094e35e","cross_cats_sorted":["cs.LG","cs.NA","math.IT","math.NA","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-11-15T12:24:49Z","title_canon_sha256":"fc251b320a1c6e6433c9683f1d114c866fabfdf378e9c5b84d554a693f9d86ad"},"schema_version":"1.0","source":{"id":"1711.05519","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.05519","created_at":"2026-06-04T19:11:14Z"},{"alias_kind":"arxiv_version","alias_value":"1711.05519v4","created_at":"2026-06-04T19:11:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.05519","created_at":"2026-06-04T19:11:14Z"},{"alias_kind":"pith_short_12","alias_value":"FPAGRIXU6LFY","created_at":"2026-06-04T19:11:14Z"},{"alias_kind":"pith_short_16","alias_value":"FPAGRIXU6LFYJD4C","created_at":"2026-06-04T19:11:14Z"},{"alias_kind":"pith_short_8","alias_value":"FPAGRIXU","created_at":"2026-06-04T19:11:14Z"}],"graph_snapshots":[{"event_id":"sha256:d4756a49ac748c1ee0cb0f8d254d8eb34f05632739c1314e5779a73b26286165","target":"graph","created_at":"2026-06-04T19:11:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1711.05519/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We study robust PCA for the fully observed setting, which is about separating a low rank matrix $\\boldsymbol{L}$ and a sparse matrix $\\boldsymbol{S}$ from their sum $\\boldsymbol{D}=\\boldsymbol{L}+\\boldsymbol{S}$. In this paper, a new algorithm, dubbed accelerated alternating projections, is introduced for robust PCA which significantly improves the computational efficiency of the existing alternating projections proposed in [Netrapalli, Praneeth, et al., 2014] when updating the low rank factor. The acceleration is achieved by first projecting a matrix onto some low dimensional subspace before ","authors_text":"HanQin Cai, Jian-Feng Cai, Ke Wei","cross_cats":["cs.LG","cs.NA","math.IT","math.NA","math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-11-15T12:24:49Z","title":"Accelerated Alternating Projections for Robust Principal Component Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.05519","kind":"arxiv","version":4},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f502a916483fbdc5e252e3a04ebd09dac81722130511ac10673248c9b0192327","target":"record","created_at":"2026-06-04T19:11:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e7dc15b740888359f3346294c2b9f790c6c2e2c3e6cc8f450bb3ae7b3094e35e","cross_cats_sorted":["cs.LG","cs.NA","math.IT","math.NA","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-11-15T12:24:49Z","title_canon_sha256":"fc251b320a1c6e6433c9683f1d114c866fabfdf378e9c5b84d554a693f9d86ad"},"schema_version":"1.0","source":{"id":"1711.05519","kind":"arxiv","version":4}},"canonical_sha256":"2bc068a2f4f2cb848f824a230bafe73fc35e752338f771bf8bd985494db586bc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2bc068a2f4f2cb848f824a230bafe73fc35e752338f771bf8bd985494db586bc","first_computed_at":"2026-06-04T19:11:14.832029Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-04T19:11:14.832029Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GREE93eQLkGGOx7nrFaj/CPwfZkev3z5lqkk96j6GwX6Un3cuRMmY61lFNMPUkg+Kd9Tsj53FGcv9YY7OJTgCg==","signature_status":"signed_v1","signed_at":"2026-06-04T19:11:14.832478Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.05519","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f502a916483fbdc5e252e3a04ebd09dac81722130511ac10673248c9b0192327","sha256:d4756a49ac748c1ee0cb0f8d254d8eb34f05632739c1314e5779a73b26286165"],"state_sha256":"604070b8d0ce0c2649d8ddcba2a0099e32e5d4e547ce2196f0a302c52177beac"}